Separating seismic diffractions by an improved Cook-distance singular value decomposition method

The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed, and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

There are various analysis methods for CT perfusion (CTP). Although the advantages of Bayesian estimation algorithms have been newly suggested, comparisons with other analysis methods on clinical data are still limited. In this study, we compared the Bayesian estimation method with the singular value decomposition (SVD) method in the evaluation of patients with acute cerebral infarction and examined its usefulness. CTP data from 13 patients with acute stroke were analyzed using the SVD and Bayesian estimation methods implemented in Vitrea. Evaluation of visual clarity of the ischemic area and quantitative values of the healthy side-affected side ratio using the mean values of the left and right region of interest (ROI) on the images were compared using the SVD and Bayesian estimation methods. In visual evaluation, there were significant differences in CBV in four cases, and in CBF, MTT, and TTP in many cases. The healthy side-affected side ratio of the SVD and Bayesian estimation methods were as follows: CBF 1.19, 1.84; CBV 1.09, 1.02; MTT 1.12, 1.79; and TTP 1.48, 1.19. For CBF and MTT, the Bayesian estimation method had a larger ratio of the healthy side to the affected side, and for TTP, the SVD method had a larger ratio of the test side to the affected side. We suggest that the Bayesian estimation method is more useful than the SVD method for assessing CBF and MTT in CTP analysis of patients with acute stroke.

A new method for evaluating the decay ratios in a boiling water reactor (BWR) using the singular value decomposition (SVD) method had been proposed. In this method, a signal component closely related to the BWR stability can be extracted from independent components of the neutron noise signal decomposed by the SVD method. However, real-time stability monitoring by the SVD method requires an efficient procedure for screening such compoments. For efficient screening, an artificial neural network (ANN) with three layers was adopted. The trained ANN was actually applied to decomposed components of local power range monitor (LPRM) signals that were measured in stability experiments conducted in the Ringhals-1 BWR. In each LPRM signal, multiple candidates were screened from the decomposed components. However, decay ratios could be estimated by introducing appropriate criterions for selecting the most suitable component among the candidates. The estimated decay ratios are almost identical to those evaluated by visual screening in a previous study. The selected components commonly have the largest singular value, the largest decay ratio and the least squared fitting error among the candidates. By virtue of excellent screening performance of the trained ANN, the real-time stability monitoring by the SVD method can be applied in practice.

Pretreatment ischemic core volume is conceptually equal to follow-up infarct volume (FIV) in patients with successful recanalization. However, there is sometimes an absolute volume difference (AD) between pretreatment core volume and FIV. The aim was to compare the AD values between the Bayesian and the singular value decomposition (SVD) methods with time from onset-to-imaging in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy. Consecutive AIS patients were included if they had the following: (1) anterior large vessel occlusion (internal carotid or middle cerebral artery); (2) within 24h of onset; (3) pretreatment CT perfusion (CTP); (4) successful recanalization (mTICI ≥ 2b); and (5) 24-h diffusion-weighted imaging (DWI). FIV was measured on 24-h DWI. The AD value between FIV and the pretreatment core volume was calculated for Bayesian and SVD methods. Spearman's rank correlation coefficient (rho) was calculated as appropriate. In the 47 patients enrolled (25 men; median age 78years; median baseline National Institutes of Health Stroke Scale, 22), the median time from onset-to-imaging and onset-to-recanalization was 136 and 220min, respectively. Shorter onset-to-imaging time was correlated with a larger AD value, and more trend was seen in the SVD method (rho = - 0.28, p = 0.05) compared with the Bayesian method (rho = - 0.08). A larger pretreatment core volume was correlated with a larger AD value, and this tendency was slightly stronger for the SVD (rho = 0.63, p < 0.01) than for the Bayesian (rho = 0.32, p = 0.03) method. The Bayesian method might be more correlated with FIV than the SVD method in patients with a large ischemic lesion immediately after stroke onset, but not perfect.

An improved ridge estimation (IRE) method for troposphere water vapor tomography

Seismic wavefield information extraction method based on adaptive local singular value decomposition

Singular value decomposition (SVD) method is a very important matrix decomposition method in linear algebra. It is widely used in signal processing, statistics, data compression and other fields. The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique, and then presents new methods for dual private protection data mining. Finally we conduct experiments to test and verify the proposed approach and get good results.

Deterioration of insulation materials caused by partial discharge is one of the important reasons for the failure of power equipment. The noise caused by the interference of the external electromagnetic environment seriously affects the accuracy of partial discharge detection. Aiming at the problem of partial discharge noise suppression, this paper proposed a partial discharge signal noise suppression method based on adaptive window singular value decomposition. This method first randomly generated a series of windows with different positions and lengths on the noisy partial discharge signal; Then singular value decomposition method was employed to denoise the signals in each window; Finally, the denoising index was chosen as the fitness function, through the particle swarm algorithm to obtain the optimal solution of the window position and length. The noise signals obtained by simulation and experiment were verified, and the method was compared with adaptive singular value decomposition, wavelet denoising, and other methods. The results showed that this method could achieve a better denoising effect.

We monitored the occupancy of a functionally important non-coordinated water molecule in the distal heme pocket of sperm whale myoglobin over the pH range 4.3-9.4. Water occupancy was assessed by using time-resolved spectroscopy to detect the perturbation of the heme visible band absorption spectrum caused by water entry after CO photodissociation ( Goldbeck, R. A., Bhaskaran, S., Ortega, C., Mendoza, J. L., Olson, J. S., Soman, J., Kliger, D. S., and Esquerra, R. M. (2006) Proc. Natl. Acad. Sci. U. S. A. 103, 1254-1259 ). We found that the water occupancy observed during the time interval between ligand photolysis and diffusive recombination decreased by nearly 20% as the pH was lowered below 6. This decrease accounted for most of the concomitant increase in the observed CO bimolecular recombination rate constant, as the lower water occupancy presented a smaller kinetic barrier to CO entry into the pocket at lower pH. These results were consistent with a model in which the distal histidine, which stabilizes the water molecule within the distal pocket by accepting a hydrogen bond, tends to swing out of the pocket upon protonation and destabilize the water occupancy at low pH. Extrapolation of this model to lower pH suggests that the additional increase in ligand association rate constant observed previously in stopped-flow studies at pH 3 may also be due in part to reduced distal water occupancy concomitant with further His64 protonation and coupled protein conformational change.

To determine the most suitable postprocessing technique for magnetic resonance (MR) perfusion imaging in patients with vascular stenosis, by comparing the cerebral blood flow (CBF) maps of single photon emission tomography (SPECT) and perfusion MR imaging (MRI). In 15 consecutive patients (14 men and one woman, mean age 73.9 +/- 6.0 years) with stenosis of common carotid artery (CCA) or internal carotid artery (ICA) of more than 75%, both brain perfusion MRI and brain perfusion SPECT were performed. From perfusion MR images, CBF maps were calculated with the first moment, singular value decomposition (SVD), and block circulant SVD (b-SVD) methods, and CBF maps from each algorithm were compared with those from SPECT. The b-SVD method had the best correlation with SPECT (R = 0.814), followed by the first moment method (R = 0.776) and the SVD method (R = 0.723). The b-SVD method has the least mean difference with SPECT (0.118), the first moment method also had less difference (0.121), and the SVD had greatest mean difference (0.164). Our results suggest that in patients with vascular impairment the b-SVD method will be the technique of choice rather than SVD or first moment method.

Subsidence monitoring in mining areas is crucial for environmental protection in mining regions. The small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique has proven highly effective for monitoring ground subsidence. However, solutions based on the SBAS-InSAR deformation model typically assume linear deformation, which limits their ability to capture the nonlinear, S-shaped subsidence patterns commonly observed in mining areas. A method referred to as Boltzmann time function-constrained SBAS-InSAR (BTFC-SBAS) is proposed to effectively address this limitation by introducing the Boltzmann function as a constraint into the SBAS-InSAR deformation model. This method incorporates a nonlinear functional constraint that aligns with the deformation characteristics of mining areas. Based on the small-baseline combination strategy, the BTFC-SBAS method establishes a mapping relationship between the interferometric phase of individual ground points and nonlinear subsidence by incorporating the Boltzmann function in place of the traditional linear deformation assumption. The model parameters are estimated on a per-pixel basis using a genetic algorithm, enabling accurate inversion of the nonlinear subsidence process in mining areas. Both simulated and real-data experiments based on the Mengkeqing Mine in Ordos City are conducted to verify the feasibility of the proposed BTFC-SBAS method. In the simulation experiment, six subsidence curves with different shapes are designed, and their inversion results obtained using the BTFC-SBAS, least squares (LS), and singular value decomposition (SVD) methods are compared. The BTFC-SBAS method outperforms both the LS and SVD methods under all scenarios, achieving an average RMSE of 9.833 mm, lower than those of the SVD method (17.324 mm) and the LS method (44.158 mm). In the real-data experiment, the monitoring results derived from the BTFC-SBAS and traditional SBAS methods show highly consistent spatial patterns. With levelling data as a reference, comparative analysis at four representative validation points reveals that the RMSE of the BTFC-SBAS method decreases by 25.6%, 19.9%, 30.4%, and 29.7% relative to that of the SBAS method. These findings confirm that the BTFC-SBAS method provides higher inversion accuracy and produces time-series subsidence curves that more closely match the actual measurements. Overall, the proposed method offers an effective approach for dynamic monitoring and prediction of nonlinear surface subsidence in mining areas.

The 3D reconstruction and modeling of face, objects and surrounding environment etc., has gained significant attention from the research community in recent days. Availability of depth data from various active and passive sensors have accelerated the work in 3D domain. It finds various applications in robotics, Human Computer Interaction (HCI), medical analysis etc. The major challenge in this area is accurate and real time 3D pose estimation which forms the basis for alignment of multi-view point cloud data. Noise in the depth data due to sensor irregularities significantly affects the accuracy of pose estimation. The main idea of this paper is to compute 2D A-KAZE feature correspondence and map to 3D for obtaining more reliable 3D sparse points. The 2D-3D correspondence is used to estimate accurate pose by formulating a graph optimization problem. The experimental results of this approach is evaluated against well known Iterative Closest Point (ICP) and Singular Value Decomposition (SVD) methods for point cloud registration on face and object data obtained using stereo camera. The results shows that the algorithm is able to estimate the accurate pose with least alignment error as compared with ICP and SVD methods.

In this paper, a new receiver for downlink multicarrier code-division multiple-access (MC-CDMA) system is proposed when the channel between the transmitter and each user is multiple-input multiple-output (MIMO). The structure of the proposed receiver is based on the singular value decomposition (SVD) method. The conventional SVD method is able to eliminate the co-space interference (CSI) in single user scenario, however, due to multi-channel environment, it is not able to eliminate the co-space interferences caused by the transmitted signals to the other users that is called multiuser interference (MUI). By combining the SVD and space frequency spreading code, a receiver called total interference cancellation (TIC) is developed that completely eliminates both the CSI and the MUI. The performance of the TIC suffers from small singular values of the subchannel matrices. To improve the performance, a symbol-chip level MMSE receiver is developed in which joint equalization and beamforming is utilized at chip level when the MMSE criterion is employed at symbol level (after despreading). The performance of the proposed receiver is evaluated and compared under different scenarios by computer simulations. The results show that there is no error floor in the bit error rate (BER) performances of the SVD based receiver under both criteria in the multiuser environment. Compared with the TIC criterion, the receiver designed based on the symbol-chip level MMSE criterion significantly improves the BER performance.

This paper describes a novel application of singular value decomposition (SVD) of subsets of the phase-space trajectory for calculation of the attractor dimension of a small data set. A certain number of local centres (M) are chosen randomly on the attractor and an adequate number of nearest neighbours (q=50) are ordered around each centre. The local intrinsic dimension of a local centre is determined by the number of significant singular values and the attractor dimension (D 2) by the average of the local intrinsic dimensions of the local centres. The SVD method has been evaluated for model data and EEG. The results indicate that the SVD method is a reliable approach for estimation of attractor dimension at moderate signal to noise ratios. The paper emphasises the importance of SVD approach to EEG analysis.

Purpose:Baseline distortions in NMR spectra are caused by the corruption of the first few data points in Free Induction Decay (FID) or originated from macromolecules and lipids. To study the Singular Value Decomposition (SVD) based baseline correction method.Methods:In‐house Singular Value Decomposition (SVD) program was developed with Matlab version 7.6 (Mathworks.com). Hankel SVD method (singular value decomposition of the acquired FID signal arranged in a Hankel matrix) is used to compute the signal poles and amplitude, and from them the signal frequencies and damping factors. The estimated FID signals are constructed from the quantification parameters; residue error spectrum was calculated by FFT of the difference of original FID and estimated FID. In order to study the baseline correction effect, known simulated FID with added noise to mimic known metabolites concentration was analyzed with SVD, quantification results were compared with known reference; then baseline distortion was introduced by removing the first 5 data points, after baseline corrected with SVD method, the quantification results were compared with original reference without baseline distortion.Known patient FID acquired with Siemens Verio 3T scanner was introduced baseline distortion by removing the first 3 data points, after baseline corrected with SVD method, corrected FID were compared with original results without baseline distortion.Results:Quantification results for simulated data were compared with reference, all within 10% deviation with SNR 20.For FID acquired with Siemens Verio 3T scanner, distorted FID and calculated baseline with SVD method was compared, the baseline matches the distortion well. Corrected FID were used for quantification, the quantification results match the corrected FID well.Conclusion:As demonstrated, SVD based baseline correction method can be used to correct the FID data point missing type of distortion, results are satisfactory for both simulated data and acquired patient data.